Related Experiment Video
Updated: Jan 8, 2026
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
Published on: September 6, 2024
Ventilation heterogeneity across A-B-E phenotypes in COPD: insights from spirometry and electrical impedance
Junsong Zhang1, Tian Liu2, Zhongkai Chang3
1Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Purpose:
This study aimed to evaluate the regional ventilation distributions in A-B-E phenotypes among patients with chronic obstructive pulmonary disease (COPD). The feasibility to better distinguish the phenotypes combining global spirometry and regional ventilation parameters derived from electrical impedance tomography (EIT) was explored.
Methods:
A cohort undergoing pulmonary function testing was prospectively enrolled. Regional spatial and temporal ventilation parameters were calculated with EIT. Principal component analysis was used to visualize phenotypic clustering, while multinomial logistic regression evaluated discriminatory performance. Feature importance was interpreted using SHapley Additive exPlanations (SHAP).
Results:
This study enrolled 88 COPD patients (Group A n = 36, Group B n = 21, Group E n = 31). Spirometry and EIT parameters revealed significant intergroup differences in FEV1%pred (P < 0.001), FEV1/FVC (P < 0.001), GI-FEV1 (regional distribution of FEV1%pred in functional EIT; P = 0.004), GI-FEV1/FVC (regional distribution of FEV1/FVC; P = 0.001) and expiratory time constant (P = 0.017). Group A demonstrated the best pulmonary function (FEV1%pred: 77.67 ± 20.40), while Group E showed the most pronounced flow limitation (longest time required to exhale 75% of FVC, T75). The multinomial model showed optimal discrimination for Group A (AUC: 0.827), while differentiation between Groups B and E was less satisfactory (AUC: 0.749). SHAP analysis identified FEV1%pred as the most significant predictor (|SHAP| = 0.477), with EIT-derived parameters GI-FEV1/FVC (|SHAP| = 0.203) and regional T75 (|SHAP| = 0.189) providing substantial incremental value.
Conclusion:
COPD phenotypes showed differences in global and regional flow limitations. The combination of global and regional information helped with distinguishing phenotypes.
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